schain Commit - r1541:77e2d8d2b2ef · Jicamarca Repository

para pruebas em PC AMISR

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r1541:77e2d8d2b2ef

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r1541:77e2d8d2b2ef

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schainpy/model/io/jroIO_kamisr.py +2 -1

                         self.dataOut.flagNoData = True
                         return 0
-                    if self.__hasNotDataInBuffer():
+                    if self.profileIndex >= (self.newProfiles*self.nblocks): #
+                    #if self.__hasNotDataInBuffer():
                         if not (self.readNextFile(self.online)):
                             return 0

schainpy/model/proc/jroproc_spectra.py +16 -6

                     self.isConfig = True
                     if offset!=None:
                         self.offset = 10**(offset/10)
-                    #print("config getNoise Done")
+                    #print("config getNoiseB Done")
                 def run(self, dataOut, offset=None, minHei=None, maxHei=None,minVel=None, maxVel=None, minFreq= None, maxFreq=None, warnings=False):
                     self.dataOut = dataOut
                     noise = None
                     if self.dataOut.type == 'Voltage':
                         noise = self.dataOut.getNoise(ymin_index=self.minIndex, ymax_index=self.maxIndex)
-                    #print(minIndex, maxIndex,minIndexVel, maxIndexVel)
+                        #print(minIndex, maxIndex,minIndexVel, maxIndexVel)
                     elif self.dataOut.type == 'Spectra':
+                        #print(self.minIndex, self.maxIndex,self.minIndexFFT, self.maxIndexFFT, self.dataOut.nIncohInt)
                         noise = numpy.zeros( self.dataOut.nChannels)
+                        norm = 1
                         for channel in range( self.dataOut.nChannels):
-                            norm = self.dataOut.max_nIncohInt/self.dataOut.nIncohInt[channel, self.minIndex:self.maxIndex]
+                            if not hasattr(self.dataOut.nIncohInt,'__len__'):
-                            #print("norm nIncoh: ", norm )
+                                norm = 1
+                            else:
+                                norm = self.dataOut.max_nIncohInt/self.dataOut.nIncohInt[channel, self.minIndex:self.maxIndex]
+                            #print("norm nIncoh: ", norm ,self.dataOut.data_spc.shape)
                             daux =  self.dataOut.data_spc[channel,self.minIndexFFT:self.maxIndexFFT, self.minIndex:self.maxIndex]
                             daux = numpy.multiply(daux, norm)
                             #print("offset: ", self.offset, 10*numpy.log10(self.offset))
                             #noise[channel] = self.getNoiseByMean(daux)/self.offset
+                            #print(daux.shape, daux)
                             noise[channel] = self.getNoiseByHS(daux, self.dataOut.max_nIncohInt)/self.offset
+                            # data = numpy.mean(daux,axis=1)
+                            # sortdata = numpy.sort(data, axis=None)
+                            # noise[channel] = _noise.hildebrand_sekhon(sortdata, self.dataOut.max_nIncohInt)/self.offset
                         #noise = self.dataOut.getNoise(xmin_index=self.minIndexFFT, xmax_index=self.maxIndexFFT, ymin_index=self.minIndex, ymax_index=self.maxIndex)
                     else:
                         noise = self.dataOut.getNoise(xmin_index=self.minIndexFFT, xmax_index=self.maxIndexFFT, ymin_index=self.minIndex, ymax_index=self.maxIndex)
                     #print("2: ",10*numpy.log10(self.dataOut.noise_estimation/64))
                     #print(self.dataOut.flagNoData)
+                    print("getNoise Done")
                     return self.dataOut
                 def getNoiseByMean(self,data):
                         self.incohInt = 0
                         self.nOutliers = 0
                         self.__profIndex = 0
+                        #print("IncohInt Done")
                     return dataOut
             class dopplerFlip(Operation):

schainpy/model/proc/jroproc_voltage.py +11 -19

                 '''
                 '''
-                #isConfig = False
-                #n = None
-                #__dataReady = False
                 __buffer_data = []
                 __buffer_times = []
-                #__initime = None
-                #__count_exec = 0
-                #__profIndex = 0
-                buffer = None
-                #lenProfileOut = 1
-                #init_prof = 0
+                buffer = None
-                #end_prof = 0
-                #first_utcBlock = None
                 outliers_IDs_list = []
-                #__dh = 0
                 __slots__ = ('n','navg','profileMargin','thHistOutlier','minHei_idx','maxHei_idx','nHeights',
                     '__dh','first_utcBlock','__profIndex','init_prof','end_prof','lenProfileOut','nChannels',
                             # plt.grid()
                             # plt.show()
                     outliers_IDs = outliers_IDs.astype(numpy.dtype('int64'))
                     outliers_IDs = numpy.unique(outliers_IDs)
                     outs_lines = numpy.sort(outliers_IDs)
                     #Agrupando el histograma de outliers,
+                    #my_bins = numpy.linspace(0,int(profiles), int(profiles/100), endpoint=False)
                     my_bins = numpy.linspace(0,9600, 96, endpoint=False)
                     hist, bins =  numpy.histogram(outs_lines,bins=my_bins)
                     hist_outliers_indexes = numpy.where(hist > self.thHistOutlier)       #es outlier
                     #print(hist_outliers_indexes[0])
                     #print(bins_outliers_indexes)
                     outlier_loc_index = []
-                    #outlier_loc_index = [k for k in range(bins_outliers_indexes[n]-50,bins_outliers_indexes[n+1]+50) for n in range(len(bins_outliers_indexes)-1) ]
-                    for n in range(len(bins_outliers_indexes)-1):
+                    # for n in range(len(bins_outliers_indexes)-1):
-                        #outlier_loc_index = [k for k in range(bins_outliers_indexes[n]-50,bins_outliers_indexes[n+1]+50)]
+                    #     for k in range(bins_outliers_indexes[n]-self.profileMargin,bins_outliers_indexes[n+1]+self.profileMargin):
-                        for k in range(bins_outliers_indexes[n]-self.profileMargin,bins_outliers_indexes[n+1]+self.profileMargin):
+                    #         outlier_loc_index.append(k)
-                            outlier_loc_index.append(k)
+                    outlier_loc_index = [e for n in range(len(bins_outliers_indexes)-1) for e in range(bins_outliers_indexes[n]-self.profileMargin,bins_outliers_indexes[n+1]+self.profileMargin) ]
                     outlier_loc_index = numpy.asarray(outlier_loc_index)
-                    #print(numpy.unique(outlier_loc_index))
+                    #print(len(numpy.unique(outlier_loc_index)), numpy.unique(outlier_loc_index))

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